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V V Zharkova, S S Ipson

Survey of feature recognition techniques Work package 5 Bradford University & Meudon Observatory. V V Zharkova, S S Ipson. Summary of the recognition techniques. P - pre-processing , I - user interaction was required and A - automated method.

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V V Zharkova, S S Ipson

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  1. Survey of feature recognition techniquesWork package 5 Bradford University & Meudon Observatory V V Zharkova, S S Ipson

  2. Summary of the recognition techniques SFR Workshop 1, BRO, Brussels, 23-24 Oct '03 P - pre-processing, I - user interaction was requiredand A - automated method

  3. II. Survey of Pattern Recognition Techniques • 3.1 Image preparation • 3.1.1 Geometrical distortion • 3.1.2 Blurring • 3.1.3 Intensity calibration • 3.1.4 Miscellaneous defects • 3.2 Detection of sunspots • 3.2.1 Histogram methods • 3.2.2 LOG methods • 3.2.3 Region growing methods • 3.2.4 Simulated annealing • 3.3 Filament detection • 3.3.1 Chain linking procedure • 3.3.2 Region growing procedure • 3.4 Detection of active regions (plage) • 3.4.1 Global intensity threshold • 3.4.2 Region growing methods • 3.4.3 Bayesian inference method • 3.5 Detection of coronal mass ejections • 3.5.1 Hough transform method • 3.5.2 Multiple abstraction level mining • method • 2.1 Histogram-based segmentation • 2.2. Region-based segmentation • 2.2.1 Region growing • 2.2.2 Clustering • 2.2.3 Multi-resolution transforms • 2.3 Edge-based segmentation • 2.3.1 Gradient operator based edge detection • 2.3.2 Canny edge detection • 2.3.3 Laplacian of Gaussian zero-crossing edge detection • 2.4 Artificial neural networks • 2.4.1 Standard technique • 2.4.2 Cascade-correlation architecture • 2.4.3 Evolving cascade neural networks • 2.4.4 GMDH-type neural networks • 2.4.5 Generalized regression neural networks • 2.5 Explicit-model based segmentation • 2.5.1 The Hough transform • 2.5.2 Ribbon detection • 2.6 Models based on functionals • 2.6.1 Active contours • 2.7 Bayesian inference • 2.8 Motion segmentation • 2.9 Shape analysis • 2.10 Classification SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  4. III.a. Why the pre-processing techniques? Difficulties with images: • Errors in FITS header information • Image shape (ellipse), centre and the pole coordinates • Weather transparency (clouds) and different thickness of atmosphere • Centre-to-limb darkening • Defects in data (strips, lines, intensity) SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  5. SUNSPOTSSynoptic Charts Central Meridian Synoptic Map SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  6. Image segmentation procedures • Thresholding approaches (histogram-based segmentation) • Edge-based methods (using the first or second derivatives of the spatio-temporal functions • Region growing methods (intitial starting pixel + criterion for merging) • Hybrid region growing and edge detection techniques • Neural networks (training without explicit criteria) • Global Information methods (Bayesian, functional models, Hough transform) • Miscellaneous (data clustering, simulated annealing, data mining) SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  7. General techniques • Histogram-based segmentation – • Analyse the grey-level histograms • Size of the segmented object varies with the threshold • Give good results on a uniform background • Objects had a distinct intensity range • Region-based segmentation • Region growing (start from seeds and grow regions on specified criteria) • Clustering (pixels are clustered in a feature space using any discriminating feature asociated and then connecting regions are found) • Edge-based segmentation • Relies on discontinuities in the image data to locate boundaries • But edge profile is not known • Profile can vary with edge (shading or texture) SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  8. Edge-based segmentation • Gradient operator based edge detection – • Vertical and horizontal components are finite difference formulae with • Sobel convolution masks: vertical and horizontal -1 -2 -1 -1 0 1 0 0 0 -2 0 2 1 2 1 -1 0 1 • Gradient magnitude - a square root of the sum of the square gradient components • Candidate edge located with gradient magnitude above threshold • Multi passes of the detected edge • Canny edge detection • Smooth image with a Gaussian filter • Compute gradient magnitude and orientation with finite differences • Apply non-maxima suppression to thin the gradient-magnitude edge image • Track along edges starting from the point esceeding higher threshold with the edge point esceeding the lower threshold • Apply edge linking to fill small gaps SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  9. Edge-based segmentation • Laplacian of Gaussian zero-crossing edge detection (LOG) • The Laplacian - 2D isotropic measure of the second spatial derivative of an image • L of an image has the lagest magnitudes at peaks of intensity • L of an image has zero crossings at the points of inflection • Common convolution kernels to calculate digital Laplacian: 0 1 0 1 1 1 1 -4 1 1 -8 1 0 1 0 1 1 1 • L sensitive to noise => applied after a Gaussian smoothing filter • Hence => LOG or Marr-Hildreth operator SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  10. Explicit model-based segmentation • The Hough transform (CMEs – Bergmans) • Uses an accumulator array with dimension equal the number of parameters in the family of curves to be detected • If y = ax + b, then a and b and accumulator array indices (2) correspond • Accumulator array • Ribbon detection • Modified Hough transform which includes a directions of the intensity gradient across the line or curve SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  11. Miscelleneous methods • Image cleaning (solar: shape and intensity) • Image filtering • Image enhancement (to increase a contrast) • Morphological operations (to complete the feature shape) • Others (reported by other speakers) SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  12. Artificial Neural Networks • Standard technique • Exploits a feed-forward fully connected network: input, hidden or output neurons connected by adjustable synaptic weights • The technique implies that ANN structure is well defined • It means that one must preset the input and hidden neurons • Apply suitable neuron activation function • Sigmoid activation function: y = f(x, w) = 1/(1 + exp(– w0 – Σim wi xi)), where m – number of variables x1, xm, X is the input vector, w is a synaptic weigh vector • User must choose a suitable learning algorithm • Rationally set learning rate, a number of the training epochs etc. • If ANN includes 2 hidden neurons -> back-projection algorithm provides best results SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  13. Filament recognition with ANN SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  14. Recognised filaments SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  15. Summary of the Solar Feature Recognition Methods SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  16. VII. Conclusions • WP5 is successfully implementing the project plan • Feature recognition in solar images generated a substantial interest among the IT and solar community -FR Workshop • A few novel techniques were developed for each feature (see sunspots, ARs, filaments (ANN + MO), magnetic NL) • Ongoing collaboration with the partners from Meudon, NSO, UAS, IAS and OATO • The current status – a detailed catalogue design stage SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

  17. WP5 –Feature RecognitionWork in progress • Adjustment of the FR techniques to the specifics of each catalogue with respect to the time coverage period and providers for the Unified Observing Catalogues (UOC) • Created an Access database fed by the detected sunspot feature parameters and developed a preliminary query and response pages • Preparing a Demo on the Web for your testing • http://www.cyber.brad.ac.uk/egso/ SFR Workshop 1, BRO, Brussels, 23-24 Oct '03

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